Title :
Global asymptotic stability of a larger class of delayed neural networks
Author_Institution :
Dept. of Electr. & Electron. Eng., Istanbul Univ., Turkey
Abstract :
This paper presents some new sufficient conditions for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with constant time delay. It is shown that the use of a more general type of Lyapunov-Krasovskii functional enables us to establish global asymptotic stability of a larger class of delayed neural networks than those considered in some previous papers.
Keywords :
asymptotic stability; functional equations; neural nets; GAS; Lyapunov-Krasovskii functional; constant time delay; delayed neural networks; equilibrium point; global asymptotic stability; neural networks; uniqueness; Associative memory; Asymptotic stability; Cellular networks; Cellular neural networks; Delay effects; Hopfield neural networks; Linear matrix inequalities; Neural networks; Neurons; Sufficient conditions;
Conference_Titel :
Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on
Print_ISBN :
0-7803-7761-3
DOI :
10.1109/ISCAS.2003.1206414
